Enhancing CBIR Through Feature Optimization, Combination and Selection

We present a content-based image retrieval (CBIR) method based on the combination and selection of several image features. The novelty of our approach over existing methods is threefold: we provide a statistical optimization of the similarity distance for each feature; we replace certain features by a selection in a non-linear expansion of them; and we perform a linear combination of the features. We demonstrate superior capabilities of our method in certain cases over support vector machines (SVM) on a COREL image collection.